Mercurial > repos > guerler > springsuite
view planemo/lib/python3.7/site-packages/rdflib/void.py @ 1:56ad4e20f292 draft
"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author | guerler |
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date | Fri, 31 Jul 2020 00:32:28 -0400 |
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import collections from rdflib import URIRef, Graph, Literal from rdflib.namespace import VOID, RDF def generateVoID(g, dataset=None, res=None, distinctForPartitions=True): """ Returns a new graph with a VoID description of the passed dataset For more info on Vocabulary of Interlinked Datasets (VoID), see: http://vocab.deri.ie/void This only makes two passes through the triples (once to detect the types of things) The tradeoff is that lots of temporary structures are built up in memory meaning lots of memory may be consumed :) I imagine at least a few copies of your original graph. the distinctForPartitions parameter controls whether distinctSubjects/objects are tracked for each class/propertyPartition this requires more memory again """ typeMap = collections.defaultdict(set) classes = collections.defaultdict(set) for e, c in g.subject_objects(RDF.type): classes[c].add(e) typeMap[e].add(c) triples = 0 subjects = set() objects = set() properties = set() classCount = collections.defaultdict(int) propCount = collections.defaultdict(int) classProps = collections.defaultdict(set) classObjects = collections.defaultdict(set) propSubjects = collections.defaultdict(set) propObjects = collections.defaultdict(set) for s, p, o in g: triples += 1 subjects.add(s) properties.add(p) objects.add(o) # class partitions if s in typeMap: for c in typeMap[s]: classCount[c] += 1 if distinctForPartitions: classObjects[c].add(o) classProps[c].add(p) # property partitions propCount[p] += 1 if distinctForPartitions: propObjects[p].add(o) propSubjects[p].add(s) if not dataset: dataset = URIRef("http://example.org/Dataset") if not res: res = Graph() res.add((dataset, RDF.type, VOID.Dataset)) # basic stats res.add((dataset, VOID.triples, Literal(triples))) res.add((dataset, VOID.classes, Literal(len(classes)))) res.add((dataset, VOID.distinctObjects, Literal(len(objects)))) res.add((dataset, VOID.distinctSubjects, Literal(len(subjects)))) res.add((dataset, VOID.properties, Literal(len(properties)))) for i, c in enumerate(classes): part = URIRef(dataset + "_class%d" % i) res.add((dataset, VOID.classPartition, part)) res.add((part, RDF.type, VOID.Dataset)) res.add((part, VOID.triples, Literal(classCount[c]))) res.add((part, VOID.classes, Literal(1))) res.add((part, VOID["class"], c)) res.add((part, VOID.entities, Literal(len(classes[c])))) res.add((part, VOID.distinctSubjects, Literal(len(classes[c])))) if distinctForPartitions: res.add( (part, VOID.properties, Literal(len(classProps[c])))) res.add((part, VOID.distinctObjects, Literal(len(classObjects[c])))) for i, p in enumerate(properties): part = URIRef(dataset + "_property%d" % i) res.add((dataset, VOID.propertyPartition, part)) res.add((part, RDF.type, VOID.Dataset)) res.add((part, VOID.triples, Literal(propCount[p]))) res.add((part, VOID.properties, Literal(1))) res.add((part, VOID.property, p)) if distinctForPartitions: entities = 0 propClasses = set() for s in propSubjects[p]: if s in typeMap: entities += 1 for c in typeMap[s]: propClasses.add(c) res.add((part, VOID.entities, Literal(entities))) res.add((part, VOID.classes, Literal(len(propClasses)))) res.add((part, VOID.distinctSubjects, Literal(len(propSubjects[p])))) res.add((part, VOID.distinctObjects, Literal(len(propObjects[p])))) return res, dataset